CN113962788A - Logistics financial management method and system based on big data and computer storage medium - Google Patents
Logistics financial management method and system based on big data and computer storage medium Download PDFInfo
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Abstract
The application relates to a logistics financial management method, a logistics financial management system and a computer-readable storage medium based on big data, wherein the method comprises the following steps: establishing a financial management platform, and acquiring customer registration information to store and archive to generate customer information; establishing and updating star-level information for the client according to the client information; calculating order cost in real time according to the received logistics order information and the client star-level information; receiving reimbursement application information and reimbursement receipt information submitted by staff, and generating expenditure information for reimbursement expenses passing examination and approval; and carrying out statistics and archiving on the income information and the expenditure information to generate a financial statement, and carrying out statistics and archiving on the income information and the logistics order information to generate a business statement. The system and the method can realize automatic intelligent settlement, reimbursement and statistics of logistics business and finance, and calculate the logistics price in real time in a big data analysis mode, so that logistics pricing is clear and clear, service experience of customers is improved, stickiness of the customers is improved, and good development of enterprises is facilitated.
Description
Technical Field
The present application relates to the field of logistics management, and in particular, to a method, a system, and a computer storage medium for logistics financial management based on big data.
Background
Logistics refers to the overall process of planning, implementing and managing raw materials, semi-finished products, finished products or related information from the production area of a commodity to the consumption area of the commodity in transportation, storage, distribution and other ways with the lowest cost and high efficiency in order to meet the needs of customers. Logistics is a system for controlling the movement of raw materials, finished products and information from supply to the end consumer via the transfer and possession of various intermediate links, thereby achieving the clear goal of organization. Modern logistics is a product of economic globalization and is also an important service industry for promoting economic globalization. The modern logistics industry in the world is in a steady growth situation, and Europe, America and Japan become important logistics bases in the world at present.
The existing management technology for settlement, reimbursement and statistics of the logistics financial and business information basically stays in the traditional manual accounting stage, the settlement period is long, the logistics financial and business information management is not standard, and errors are easy to occur. The logistics transportation industry with continuously enlarged scale cannot be met by depending on a manual accounting mode, even if computer management is adopted, the computer-made form or the logistics system is only used for carrying out simple statistics on receivable and payable, and the management method with single function seriously restricts the development of the logistics transportation industry and wastes precious social and human resources. And at present, the pricing system in the logistics industry is disordered, and the recipient basically carries out subjective pricing, so that the unclear pricing, the reduced customer satisfaction and the loss of the customer stickiness are easily caused. Meanwhile, invoice system is adopted for reimbursement, so that effective management and control can not be carried out on company expenses, and further financial income and expense conditions are disordered, and good development of companies is influenced.
Aiming at the related technologies, the inventor thinks that the financial management of the existing logistics system is disordered, which causes the financial balance confusion and easily affects the enterprise development.
Disclosure of Invention
In order to solve the problems that financial balance confusion is caused by financial management confusion of a logistics system and enterprise development is easily influenced, the application provides a logistics financial management method and system based on big data and a computer storage medium.
In a first aspect, the present application provides a logistics financial management method based on big data, which adopts the following technical scheme:
a logistics financial management method based on big data comprises the following steps:
establishing a financial management platform, acquiring client registration information, storing and archiving the client registration information to generate client information, generating one-to-one corresponding serial numbers, and uploading the serial numbers to a cloud disk for storage;
establishing and updating star level information for the client according to the client information, and correspondingly establishing star level discount coefficients of all star levels;
receiving logistics order information sent by a client, calculating order cost in real time according to the received logistics order information and the client star-level information, and sending order cost and cost calculation details to the client;
after the customer confirms the order fee and pays, generating income information and a logistics distribution list, and sending the logistics distribution list to a logistics distribution system;
receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement cost is paid, carrying out approval, and generating expenditure information for reimbursement cost after approval;
and carrying out statistics and archiving on the income information and the expenditure information to generate a financial statement, and carrying out statistics and archiving on the income information and the logistics order information to generate a business statement.
By adopting the technical scheme, automatic intelligent settlement, reimbursement and statistics of logistics business and finance are realized, the use of numerous logistics operation managers can be met, and the operation management level of the whole logistics transportation industry can be improved. The client information can be stored and filed according to the registration information of the client and uploaded to a prejudgment for storage, so that the safety of the client information can be effectively ensured, and a solid foundation is laid for the good development of companies. Meanwhile, the star level is divided for the client, so that the client viscosity can be effectively improved, and good economic benefit is obtained. The logistics benchmark price is established in a big data analysis mode, and then other elements influencing logistics operation are analyzed in a big data analysis mode, so that logistics and pricing are clear and clear, service experience of customers is improved, stickiness of the customers is further improved, and good development and development of enterprises are facilitated; meanwhile, the reimbursement application in the enterprise is approved, so that the income and expenditure financial conditions of the enterprise are clear and standardized, and the effect of promoting the good development of the enterprise is achieved.
Preferably, the customer information includes customer name information, customer type information, contact address information, contact information and consumption record information, and the customer type information includes personal type information and enterprise type information.
By adopting the technical scheme, the perfect information of the client can be obtained when the client registers, the convenience of contact between logistics enterprises and the client is improved, the client can be informed of relevant logistics service information in time, the logistics experience and the communication clarity of the client are improved, and the effect of improving the viscosity of the client is achieved.
Preferably, the establishing and updating the star-level information for the client according to the client information specifically includes:
receiving registration information of a client and setting default initial star level for the client;
the staff verifies the identity information and the authority and then performs star-level adjustment on the client;
and updating the star information for the client according to the consumption record of the client and a preset star promotion rule.
By adopting the technical scheme, the viscosity of the client can be effectively improved, the multiple cooperation of the client is promoted, the business volume of an enterprise is effectively improved, and the effect of promoting the good development of the enterprise is achieved.
Preferably, the step of calculating the order fee in real time according to the received logistics order information and the client star-level information specifically comprises the following steps:
receiving logistics order information and client star-level information, wherein the logistics order information comprises delivery address information, receiving address information, cargo type information, cargo weight information, cargo volume information and required delivery time information;
collecting logistics price information of different areas, removing the highest value and the lowest value, taking an average value to generate suggested logistics price information of the areas, summarizing and generating suggested logistics price tables, and setting logistics reference prices of the different areas by an administrator with reference to the suggested price tables;
collecting distribution time data of logistics transportation in different areas, generating a predicted distribution time database, comparing the required delivery time information with predicted time generated in the predicted distribution time database according to logistics order information, and generating a time coefficient;
receiving the weather condition of a dispatching area in real time and matching a preset weather coefficient according to the real-time weather condition;
calculating an order benchmark price according to the logistics order information and the preset logistics benchmark price, and calculating the order cost in real time according to the order benchmark price by using a specific calculation formula as follows: order benchmark price weather coefficient star discount coefficient = order cost.
By adopting the technical scheme, the logistics benchmark price is established in a big data analysis mode and then the big data analysis mode is used for analyzing other elements influencing logistics operation, so that logistics and pricing are clear, reasonable, clear and visual, the phenomenon that a client generates wrong cognition on the logistics price is avoided, the service experience of the client is improved, the stickiness of the client is further improved, and good development and development of enterprises are facilitated.
Preferably, the time coefficient is set to 1 by default when the customer makes no request for the arrival time or the requested arrival time is greater than the expected delivery time, the calculation formula of the time coefficient when the customer provides the requested arrival time less than the expected delivery time is time coefficient = requested arrival time/expected delivery time, and the manager may manually set the maximum value of the time coefficient after verifying the identity.
Through adopting above-mentioned technical scheme, through the setting of time coefficient, under the prerequisite that satisfies customer's demand of exigency, effectively guaranteed the economic benefits of commodity circulation enterprise, improved the rationality of commodity circulation pricing simultaneously greatly, avoid appearing the phenomenon that the price is unified indiscriminate charges, suggestion customer's commodity circulation is experienced, and reinforcing customer's sense of identity reaches the effect that improves enterprise core competitiveness.
Preferably, the receiving the weather condition of the dispatch area in real time and matching the preset weather coefficient according to the real-time weather condition specifically include: the method comprises the steps of acquiring weather forecasts of a receiving point, a delivery point and a logistics trunk road city in real time, defaulting the weather coefficient to be 1 if severe weather forecasts do not appear in a delivery time period, and setting the weather coefficient to be a preset weather coefficient if severe weather forecasts appear in the delivery time period, wherein the preset weather coefficient is larger than 1.
Through adopting above-mentioned technical scheme, through the setting of weather coefficient for commodity circulation pricing is forbidden more, can make the customer agree with the reason of commodity circulation transportation price range when guaranteeing the income of commodity circulation enterprise, promotes customer's viscidity, improves the economic benefits of commodity circulation enterprise.
Preferably, the receiving the reimbursement application information and reimbursement document information submitted by the employee when reimbursement fee is paid, performing approval, and generating expenditure information for reimbursement fee expenditure passing the approval specifically includes the following steps:
receiving reimbursement application information and reimbursement document information submitted when the staff reimburse the expenses;
acquiring oil consumption data of each time period of each vehicle through an oil consumption GPS preset on the vehicle, acquiring oil price data of each place in real time to generate oil price data, and generating an oil fee reimbursement range by combining the oil price data;
receiving a vehicle maintenance record and an accessory replacement record to generate a vehicle maintenance report, acquiring official quotation information of vehicle accessory maintenance and vehicle maintenance in real time to generate a maintenance reimbursement database, and generating a predicted maintenance interval time interval and an accessory cost reimbursement range by combining the maintenance reimbursement database with the vehicle maintenance record;
checking the reimbursement application information and the reimbursement document information by combining the oil fee reimbursement range, and approving the oil fee reimbursement data in the reimbursement application information and the reimbursement document information which are positioned in the oil fee reimbursement range;
checking the reimbursement application information and the reimbursement receipt information by combining the maintenance interval time interval and the accessory expense reimbursement range, and approving the application that the vehicle maintenance time and the vehicle maintenance expense in the reimbursement application information and the reimbursement receipt information are both located in the maintenance interval time interval and the accessory expense reimbursement range;
and (4) inputting the approved reimbursement cost into an account of the applicant, and generating expenditure information for the approved reimbursement cost.
Through adopting above-mentioned technical scheme, realize intelligent settlement commodity circulation reimbursement flow, through examining and approving reimbursement expense information and reimbursement document information for whole reimbursement flow is perfect and sound, makes the expenditure of commodity circulation enterprise wash more clearly and definitely simultaneously, promotes the orderliness of financial expenditure, can effectively avoid simultaneously opening many reports and frequently reimburses the bad phenomenon of harm enterprise interests such as.
In a second aspect, the present application provides a logistics financial management system based on big data, which adopts the following technical scheme:
a big data based logistics financial management system comprising:
the server module is used for establishing a financial management platform, acquiring client registration information, storing and archiving the client registration information to generate client information, generating one-to-one corresponding serial numbers, and uploading the serial numbers to the cloud disk for storage;
the star level management module is used for establishing and updating star level information for the client according to the client information and correspondingly establishing a star level discount coefficient of each star level;
the cost calculation module is used for receiving logistics order information sent by a client, calculating order cost in real time according to the received logistics order information and the client star-level information, and sending order cost and cost calculation details to the client;
the expense settlement module is used for generating income information and a logistics distribution list after a customer confirms the order expense and pays, and sending the logistics distribution list to a logistics distribution system;
the expense reimbursement module is used for receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement expenses are paid, carrying out approval and generating expense information for reimbursement expenses passing the approval;
and the report counting module is used for counting and archiving the income information and the expenditure information to generate a financial report, and counting and archiving the income information and the logistics order information to generate a business report. .
By adopting the technical scheme, automatic intelligent settlement, reimbursement and statistics of logistics business and finance are realized, the use of numerous logistics operation managers can be met, and the operation management level of the whole logistics transportation industry can be improved. The client information can be stored and filed according to the registration information of the client and uploaded to a prejudgment for storage, so that the safety of the client information can be effectively ensured, and a solid foundation is laid for the good development of companies. Meanwhile, the star level is divided for the client, so that the client viscosity can be effectively improved, and good economic benefit is obtained. The logistics benchmark price is established in a big data analysis mode, and then other elements influencing logistics operation are analyzed in a big data analysis mode, so that logistics and pricing are clear and clear, service experience of customers is improved, stickiness of the customers is further improved, and good development and development of enterprises are facilitated; meanwhile, the reimbursement application in the enterprise is approved, so that the income and expenditure financial conditions of the enterprise are clear and standardized, and the effect of promoting the good development of the enterprise is achieved.
Preferably, the fee calculation module includes:
the information statistics module is used for receiving logistics order information and customer star-level information, wherein the logistics order information comprises delivery address information, receiving address information, cargo type information, cargo weight information, cargo volume information and required delivery time information;
the big data base price measuring and calculating module is used for collecting logistics price information of different areas, removing the highest value and the lowest value, taking an average value to generate suggested logistics price information of the area, summarizing the suggested logistics price information to generate a suggested logistics price table, and setting logistics base prices of the different areas by an administrator with reference to the suggested price table;
the big data time coefficient measuring and calculating module is used for collecting distribution time data for logistics transportation in different areas, generating a predicted distribution time database, comparing the required delivery time information with the predicted time generated in the predicted distribution time database according to the logistics order information, and generating a time coefficient;
the big data weather coefficient measuring and calculating module is used for receiving the weather conditions of the dispatching area in real time and matching preset weather coefficients according to the real-time weather conditions;
the expense measurement module is used for calculating an order benchmark price according to the logistics order information and the preset logistics benchmark price, and calculating the order expense in real time according to the order benchmark price by using a specific calculation formula as follows: order benchmark price weather coefficient star discount coefficient = order cost.
By adopting the technical scheme, the logistics benchmark price is established in a big data analysis mode and then the big data analysis mode is used for analyzing other elements influencing logistics operation, so that logistics and pricing are clear, reasonable, clear and visual, the phenomenon that a client generates wrong cognition on the logistics price is avoided, the service experience of the client is improved, the stickiness of the client is further improved, and good development and development of enterprises are facilitated.
In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium storing a computer program which can be loaded by a processor and which performs the method of any one of claims 1 to 7.
By adopting the technical scheme, automatic intelligent settlement, reimbursement and statistics of logistics business and finance are realized, the use of numerous logistics operation managers can be met, and the operation management level of the whole logistics transportation industry can be improved. And the logistics and pricing are clear and definite, the service experience of the client is improved, the stickiness of the client is further improved, and the enterprise can be favorably developed.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the automatic intelligent settlement, reimbursement and statistics of logistics business and finance are realized, the use of numerous logistics operation managers can be met, and the operation management level of the whole logistics transportation industry can be improved; the logistics and pricing are clear and clear, the service experience of customers is improved, the stickiness of the customers is further improved, and good development and development of enterprises are facilitated; meanwhile, the conditions of finance and business are effectively improved by counting the income and expenditure conditions and the business conditions of the enterprise, and the effect of improving the core competitiveness of the enterprise is achieved.
2. The logistics basic price is established in a big data analysis mode, and then other elements influencing logistics operation are analyzed in a big data analysis mode, so that logistics and pricing are clear, reasonable, clear and visual, the phenomenon that a client wrongly recognizes the logistics price is avoided, the service experience of the client is improved, the stickiness of the client is further improved, and good development and development of enterprises are facilitated;
3. realize intelligent settlement commodity circulation reimbursement flow, through examining and approving reimbursement expense information and reimbursement document information for whole reimbursement flow is perfect and sound, makes the expenditure of commodity circulation enterprise wash more clearly and definitely simultaneously, promotes the orderliness of financial expenditure, can effectively avoid many reports and frequently reimburses the bad phenomenon of harm enterprise interests such as selling more simultaneously.
Drawings
FIG. 1 is a block diagram of a method for logistics financial management in an embodiment of the present application;
FIG. 2 is a block diagram of a method of star level management in an embodiment of the present application;
FIG. 3 is a block diagram of a method of fee calculation in an embodiment of the present application;
FIG. 4 is a block diagram of a method of expense reimbursement in an embodiment of the present application;
FIG. 5 is a system block diagram of a logistics financial management system in an embodiment of the present application.
Description of reference numerals: 1. a server module; 2. a star level management module; 3. a fee calculation module; 31. an information statistics module; 32. a big data benchmark price measuring and calculating module; 33. a big data time coefficient measuring and calculating module; 34. a big data weather coefficient measuring and calculating module; 35. a fee calculating module; 4. a fee settlement module; 5. a charge reimbursement module; 6. and a report statistics module.
Detailed Description
The present application is described in further detail below with reference to figures 1-5.
The embodiment of the application discloses a logistics financial management method based on big data. Referring to fig. 1, a logistics financial management method based on big data includes the following steps:
s1, client registration: establishing a financial management platform, acquiring client registration information, storing and archiving the client registration information to generate client information, generating one-to-one corresponding serial numbers, and uploading the serial numbers to a cloud disk for storage; the customer information comprises customer name information, customer type information, contact address information, contact information and consumption record information, wherein the customer type information comprises personal type information and enterprise type information;
the method has the advantages that the perfect information of the client can be obtained during client registration, the convenience of contact between logistics enterprises and the client is improved, the client can be informed of related logistics service information in time conveniently, the logistics experience and the communication clarity of the client are improved, and the effect of improving the viscosity of the client is further achieved; meanwhile, the client information is uploaded to the cloud disk, so that the safety of the client information can be ensured;
s2, star level management: establishing and updating star level information for the client according to the client information, and correspondingly establishing star level discount coefficients of all star levels; the discount coefficient of each star level is set by a manager according to the operation condition of the enterprise;
s3, calculating cost: receiving logistics order information sent by a client, calculating order cost in real time according to the received logistics order information and the client star-level information, and sending order cost and cost calculation details to the client;
s4, order confirmation: after the customer confirms the order fee and pays, generating income information and a logistics distribution list, and sending the logistics distribution list to a logistics distribution system;
s5, expense reimbursement: receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement cost is paid, carrying out approval, and generating expenditure information for reimbursement cost after approval;
s6, report generation: and carrying out statistics and archiving on the income information and the expenditure information to generate a financial statement, and carrying out statistics and archiving on the income information and the logistics order information to generate a business statement. The automatic intelligent settlement, reimbursement and statistics of logistics business and finance are realized, the use of numerous logistics operation managers can be met, and the operation management level of the whole logistics transportation industry can be improved. The client information can be stored and filed according to the registration information of the client and uploaded to a prejudgment for storage, so that the safety of the client information can be effectively ensured, and a solid foundation is laid for the good development of companies. Meanwhile, the star level is divided for the client, so that the client viscosity can be effectively improved, and good economic benefit is obtained. The logistics benchmark price is established in a big data analysis mode, and then other elements influencing logistics operation are analyzed in a big data analysis mode, so that logistics and pricing are clear and clear, service experience of customers is improved, stickiness of the customers is further improved, and good development and development of enterprises are facilitated; meanwhile, the reimbursement application in the enterprise is approved, so that the income and expenditure financial conditions of the enterprise are clear and standardized, and the effect of promoting the good development of the enterprise is achieved.
Referring to fig. 2, the setting up and updating the star-level information for the client according to the client information in step S2 includes:
a1, setting default star level: receiving registration information of a client and setting default initial star level for the client;
a2, manually adjusting star level: the staff verifies the identity information and the authority and then performs star-level adjustment on the client; the specific means for verifying identity comprises account number password, fingerprint identification, face identification, verification code identification and the like; the star-level system is more complete, and managers can conveniently set high star levels for high-quality customers and enterprises, so that the viscosity of the customers is improved, and the good development of logistics enterprises is effectively promoted; a
A3, automatic adjustment star level: and updating the star information for the client according to the consumption record of the client and a preset star promotion rule. Wherein the star promotion rule is set by a manager according to the actual operation condition of the enterprise. The viscosity of the client can be effectively improved through the setting of the star level of the client, the multiple-time collaboration intention of the client is excited, the business volume of an enterprise is effectively improved, and the effect of promoting the good development of the enterprise is achieved.
Referring to fig. 3, the step S3 of calculating the order fee in real time according to the received logistics order information and the client star-level information specifically includes the following steps:
b1, reception information: receiving logistics order information and client star-level information, wherein the logistics order information comprises delivery address information, receiving address information, cargo type information, cargo weight information, cargo volume information and required delivery time information;
b2, calculating reference price by big data: collecting logistics price information of different areas, removing the highest value and the lowest value, taking an average value to generate suggested logistics price information of the areas, summarizing and generating suggested logistics price tables, and setting logistics reference prices of the different areas by an administrator with reference to the suggested price tables;
b3, big data measurement time coefficient: collecting distribution time data of logistics transportation in different areas, generating a predicted distribution time database, comparing the required delivery time information with predicted time generated in the predicted distribution time database according to logistics order information, and generating a time coefficient;
b4, calculating weather coefficients by big data: receiving the weather condition of a dispatching area in real time and matching a preset weather coefficient according to the real-time weather condition;
b5, calculating order cost: calculating an order benchmark price according to the logistics order information and the preset logistics benchmark price, and calculating the order cost in real time according to the order benchmark price by using a specific calculation formula as follows: order benchmark price weather coefficient star discount coefficient = order cost. The logistics benchmark price is established in the big data analysis mode, and then other elements influencing logistics operation are analyzed in the big data analysis mode, so that logistics and pricing are clear, reasonable, clear and visual, the phenomenon that a client generates wrong cognition on the logistics price is avoided, the service experience of the client is improved, the stickiness of the client is further improved, and good development and development of enterprises are facilitated.
The time coefficient in step B3 is set to 1 by default when the customer has no request for the delivery time or the requested delivery time is greater than the expected delivery time, and when the customer provides the requested delivery time less than the expected delivery time, the calculation formula of the time coefficient is: time coefficient = required delivery time/estimated delivery time, and the manager may manually set the maximum time coefficient after verifying the identity, the setting of the time coefficient depends on the logistics capacity of the enterprise, and the maximum time coefficient is 2 in this embodiment. Through the setting of time coefficient, under the prerequisite that satisfies customer's urgent demand, effectively guaranteed the economic benefits of logistics enterprise, improved the rationality of commodity circulation pricing simultaneously greatly, avoid appearing the phenomenon of the indiscriminate charge of price non-uniformity, suggestion customer's commodity circulation is experienced, and the enhancement customer's sense of identity reaches the effect that improves enterprise core competitiveness.
The step B4 of receiving the weather condition of the dispatch area in real time and matching the preset weather coefficient according to the real-time weather condition specifically includes: the method comprises the steps of acquiring weather forecasts of a receiving point, a delivery point and a logistics trunk road city in real time, defaulting the weather coefficient to be 1 if severe weather forecasts do not appear in a delivery time period, and setting the weather coefficient to be a preset weather coefficient if severe weather forecasts appear in the delivery time period, wherein the preset weather coefficient is larger than 1. Wherein the bad weather includes rainstorm weather, snowing weather and the like. After the preset weather coefficient is authenticated by the awakening authority of the administrator, the preset weather coefficient is set to be 1.2. Through the setting of weather coefficient for commodity circulation pricing is more rigorous, can make the customer agree with the reason that the commodity circulation transportation price range changes when guaranteeing the income of commodity circulation enterprise, promotes customer's viscidity, improves the economic benefits of commodity circulation enterprise.
Referring to fig. 4, the step B5 of receiving reimbursement application information and reimbursement receipt information submitted by the employee when reimbursement charges, performing approval, and generating expenditure information for reimbursement charges approved by the approval specifically includes the following steps:
c1, receiving reimbursement information: receiving reimbursement application information and reimbursement document information submitted when the staff reimburse the expenses;
c2, monitoring oil consumption, measuring and calculating the oil fee reimbursement range: acquiring oil consumption data of each time period of each vehicle through an oil consumption GPS preset on the vehicle, acquiring oil price data of each place in real time to generate oil price data, and generating an oil fee reimbursement range by combining the oil price data;
c3, monitoring vehicle maintenance records: receiving a vehicle maintenance record and an accessory replacement record to generate a vehicle maintenance report, acquiring official quotation information of vehicle accessory maintenance and vehicle maintenance in real time to generate a maintenance reimbursement database, and generating a predicted maintenance interval time interval and an accessory cost reimbursement range by combining the maintenance reimbursement database with the vehicle maintenance record;
c4, oil fee information auditing: checking the reimbursement application information and the reimbursement document information by combining the oil fee reimbursement range, and approving the oil fee reimbursement data in the reimbursement application information and the reimbursement document information which are positioned in the oil fee reimbursement range;
c5, checking maintenance information: checking the reimbursement application information and the reimbursement receipt information by combining the maintenance interval time interval and the accessory expense reimbursement range, and approving the application that the vehicle maintenance time and the vehicle maintenance expense in the reimbursement application information and the reimbursement receipt information are both located in the maintenance interval time interval and the accessory expense reimbursement range;
c6, generating expenditure information: and (4) inputting the approved reimbursement cost into an account of the applicant, and generating expenditure information for the approved reimbursement cost. Realize intelligent settlement commodity circulation reimbursement flow, through examining and approving reimbursement expense information and reimbursement document information for whole reimbursement flow is perfect and sound, makes the expenditure of commodity circulation enterprise wash more clearly and definitely simultaneously, promotes the orderliness of financial expenditure, can effectively avoid many reports and frequently reimburses the bad phenomenon of harm enterprise interests such as selling more simultaneously.
The embodiment of the application also discloses a logistics financial management system based on the big data. Referring to fig. 5, a big data-based logistics financial management system includes:
the server module 1 is used for establishing a financial management platform, acquiring client registration information, storing and archiving the client registration information to generate client information, generating one-to-one corresponding serial numbers, and uploading the serial numbers to a cloud disk for storage;
the star level management module 2 is used for establishing and updating star level information for the client according to the client information and correspondingly establishing a star level discount coefficient of each star level;
the cost calculation module 3 is used for receiving logistics order information sent by a client, calculating order cost in real time according to the received logistics order information and the client star-level information, and sending order cost and cost calculation details to the client;
the expense settlement module 4 is used for generating income information and a logistics distribution list after a customer confirms the order expense and pays, and sending the logistics distribution list to a logistics distribution system;
the expense reimbursement module 5 is used for receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement expenses are paid, carrying out approval and generating expenditure information for reimbursement expense after approval;
and the report counting module 6 is used for counting and archiving the income information and the expenditure information to generate a financial report, and counting and archiving the income information and the logistics order information to generate a business report. The automatic intelligent settlement, reimbursement and statistics of logistics business and finance are realized, the use of numerous logistics operation managers can be met, and the operation management level of the whole logistics transportation industry can be improved. The client information can be stored and filed according to the registration information of the client and uploaded to a prejudgment for storage, so that the safety of the client information can be effectively ensured, and a solid foundation is laid for the good development of companies. Meanwhile, the star level is divided for the client, so that the client viscosity can be effectively improved, and good economic benefit is obtained. The logistics benchmark price is established in a big data analysis mode, and then other elements influencing logistics operation are analyzed in a big data analysis mode, so that logistics and pricing are clear and clear, service experience of customers is improved, stickiness of the customers is further improved, and good development and development of enterprises are facilitated; meanwhile, the reimbursement application in the enterprise is approved, so that the income and expenditure financial conditions of the enterprise are clear and standardized, and the effect of promoting the good development of the enterprise is achieved.
Referring to fig. 5, the fee calculation module 3 includes:
the information statistics module 31 is configured to receive logistics order information and customer star-level information, where the logistics order information includes delivery address information, receiving address information, cargo type information, cargo weight information, cargo volume information, and required delivery time information;
the big data base price measuring and calculating module 32 is used for collecting logistics price information of different areas, removing the highest value and the lowest value, taking an average value to generate suggested logistics price information of the area, summarizing and generating a suggested logistics price table, and setting logistics base prices of different areas by an administrator with reference to the suggested price table;
the big data time coefficient measuring and calculating module 33 is used for collecting distribution time data for logistics transportation in different areas, generating a predicted distribution time database, comparing the required delivery time information with the predicted time generated in the predicted distribution time database according to the logistics order information, and generating a time coefficient;
the big data weather coefficient measuring and calculating module 34 is used for receiving the weather conditions of the distribution area in real time and matching preset weather coefficients according to the real-time weather conditions;
the cost calculating module 35 is configured to calculate an order benchmark price according to the logistics order information in combination with a preset logistics benchmark price, and calculate an order cost in real time according to the order benchmark price by using a specific calculation formula as follows: order benchmark price weather coefficient star discount coefficient = order cost. The logistics benchmark price is established in the big data analysis mode, and then other elements influencing logistics operation are analyzed in the big data analysis mode, so that logistics and pricing are clear, reasonable, clear and visual, the phenomenon that a client generates wrong cognition on the logistics price is avoided, the service experience of the client is improved, the stickiness of the client is further improved, and good development and development of enterprises are facilitated.
The embodiment of the present application further discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and executed in the method as described above, and the computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above examples are only used to illustrate the technical solutions of the present invention, and do not limit the scope of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from these embodiments without making any inventive step, fall within the scope of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art may still make various combinations, additions, deletions or other modifications of the features of the embodiments of the present invention according to the situation without conflict, so as to obtain different technical solutions without substantially departing from the spirit of the present invention, and these technical solutions also fall within the protection scope of the present invention.
Claims (10)
1. A logistics financial management method based on big data is characterized by comprising the following steps:
establishing a financial management platform, acquiring client registration information, storing and archiving the client registration information to generate client information, generating one-to-one corresponding serial numbers, and uploading the serial numbers to a cloud disk for storage;
establishing and updating star level information for the client according to the client information, and correspondingly establishing star level discount coefficients of all star levels;
receiving logistics order information sent by a client, calculating order cost in real time according to the received logistics order information and the client star-level information, and sending order cost and cost calculation details to the client;
after the customer confirms the order fee and pays, generating income information and a logistics distribution list, and sending the logistics distribution list to a logistics distribution system;
receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement cost is paid, carrying out approval, and generating expenditure information for reimbursement cost after approval;
and carrying out statistics and archiving on the income information and the expenditure information to generate a financial statement, and carrying out statistics and archiving on the income information and the logistics order information to generate a business statement.
2. The logistics financial management method based on big data as claimed in claim 1, wherein: the customer information comprises customer name information, customer type information, contact address information, contact information and consumption record information, and the customer type information comprises personal type information and enterprise type information.
3. The method as claimed in claim 2, wherein the establishing and updating the star information for the client according to the client information specifically comprises:
receiving registration information of a client and setting default initial star level for the client;
the staff verifies the identity information and the authority and then performs star-level adjustment on the client;
and updating the star information for the client according to the consumption record of the client and a preset star promotion rule.
4. The logistics financial management method based on big data as claimed in claim 1, wherein: the step of calculating the order cost in real time according to the received logistics order information and the client star-level information specifically comprises the following steps:
receiving logistics order information and client star-level information, wherein the logistics order information comprises delivery address information, receiving address information, cargo type information, cargo weight information, cargo volume information and required delivery time information;
collecting logistics price information of different areas, removing the highest value and the lowest value, taking an average value to generate suggested logistics price information of the areas, summarizing and generating suggested logistics price tables, and setting logistics reference prices of the different areas by an administrator with reference to the suggested price tables;
collecting distribution time data of logistics transportation in different areas, generating a predicted distribution time database, comparing the required delivery time information with predicted time generated in the predicted distribution time database according to logistics order information, and generating a time coefficient;
receiving the weather condition of a dispatching area in real time and matching a preset weather coefficient according to the real-time weather condition;
calculating an order benchmark price according to the logistics order information and the preset logistics benchmark price, and calculating the order cost in real time according to the order benchmark price by using a specific calculation formula as follows: order benchmark price weather coefficient star discount coefficient = order cost.
5. The logistics financial management method based on big data as claimed in claim 4, wherein the time coefficient is set to 1 by default when the customer does not make a request for the arrival time or the requested arrival time is greater than the expected delivery time, the calculation formula of the time coefficient when the customer provides the requested arrival time less than the expected delivery time is time coefficient = requested arrival time/expected delivery time, and the manager can set the maximum value of the time coefficient manually after verifying the identity.
6. The logistics financial management method based on big data according to claim 4, wherein the receiving weather conditions of the dispatch area in real time and matching the preset weather coefficients according to the real-time weather conditions specifically comprises: the method comprises the steps of acquiring weather forecasts of a receiving point, a delivery point and a logistics trunk road city in real time, defaulting the weather coefficient to be 1 if severe weather forecasts do not appear in a delivery time period, and setting the weather coefficient to be a preset weather coefficient if severe weather forecasts appear in the delivery time period, wherein the preset weather coefficient is larger than 1.
7. The logistics financial management method based on big data as claimed in claim 1, wherein: the method comprises the following steps of receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement cost is paid, carrying out approval, and generating expenditure information for reimbursement cost after approval specifically comprises the following steps:
receiving reimbursement application information and reimbursement document information submitted when the staff reimburse the expenses;
acquiring oil consumption data of each time period of each vehicle through an oil consumption GPS preset on the vehicle, acquiring oil price data of each place in real time to generate oil price data, and generating an oil fee reimbursement range by combining the oil price data;
receiving a vehicle maintenance record and an accessory replacement record to generate a vehicle maintenance report, acquiring official quotation information of vehicle accessory maintenance and vehicle maintenance in real time to generate a maintenance reimbursement database, and generating a predicted maintenance interval time interval and an accessory cost reimbursement range by combining the maintenance reimbursement database with the vehicle maintenance record;
checking the reimbursement application information and the reimbursement document information by combining the oil fee reimbursement range, and approving the oil fee reimbursement data in the reimbursement application information and the reimbursement document information which are positioned in the oil fee reimbursement range;
checking the reimbursement application information and the reimbursement receipt information by combining the maintenance interval time interval and the accessory expense reimbursement range, and approving the application that the vehicle maintenance time and the vehicle maintenance expense in the reimbursement application information and the reimbursement receipt information are both located in the maintenance interval time interval and the accessory expense reimbursement range;
and (4) inputting the approved reimbursement cost into an account of the applicant, and generating expenditure information for the approved reimbursement cost.
8. A logistics financial management system based on big data is characterized by comprising:
the server module (1) is used for establishing a financial management platform, acquiring client registration information, storing and archiving the client registration information to generate client information, generating one-to-one corresponding serial numbers, and uploading the serial numbers to a cloud disk for storage;
the star level management module (2) is used for establishing and updating star level information for the client according to the client information and correspondingly establishing a star level discount coefficient of each star level;
the expense calculation module (3) is used for receiving logistics order information sent by a client, calculating order expense in real time according to the received logistics order information and the client star-level information, and sending order expense and expense calculation details to the client;
the expense settlement module (4) is used for generating income information and a logistics distribution list after a customer confirms the order expense and pays, and sending the logistics distribution list to a logistics distribution system;
the expense reimbursement module (5) is used for receiving reimbursement application information and reimbursement document information submitted by staff when reimbursement expenses are paid, carrying out approval and generating expense information for reimbursement expense passing the approval;
and the report counting module (6) is used for counting and archiving the income information and the expenditure information to generate a financial report, and counting and archiving the income information and the logistics order information to generate a business report.
9. A big data based logistics financial management system according to claim 8 wherein the fee calculation module (3) comprises:
the information statistics module (31) is used for receiving logistics order information and customer star-level information, wherein the logistics order information comprises delivery address information, receiving address information, goods type information, goods weight information, goods volume information and required delivery time information;
the big data reference price measuring and calculating module (32) is used for collecting logistics price information of different areas, removing the highest value and the lowest value, taking an average value to generate suggested logistics price information of the area, summarizing and generating a suggested logistics price table, and setting logistics reference prices of the different areas by an administrator with reference to the suggested price table;
the big data time coefficient measuring and calculating module (33) is used for collecting distribution time data of logistics transportation in different areas, generating a predicted distribution time database, comparing the required delivery time information with the predicted time generated in the predicted distribution time database according to the logistics order information, and generating a time coefficient;
the big data weather coefficient measuring and calculating module (34) is used for receiving the weather conditions of the dispatching area in real time and matching preset weather coefficients according to the real-time weather conditions;
the expense measurement module (35) is used for calculating an order benchmark price according to the logistics order information and the preset logistics benchmark price, and calculating the order expense in real time according to the order benchmark price by using a specific calculation formula as follows: order benchmark price weather coefficient star discount coefficient = order cost.
10. A computer-readable storage medium characterized by: a computer program which can be loaded by a processor and which performs the method according to any of claims 1-7.
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| CN116362905A (en) * | 2023-04-08 | 2023-06-30 | 广州智卡物流科技有限公司 | Financial management method and system based on logistics industry chain |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116362905A (en) * | 2023-04-08 | 2023-06-30 | 广州智卡物流科技有限公司 | Financial management method and system based on logistics industry chain |
| CN116362905B (en) * | 2023-04-08 | 2024-03-19 | 广州智卡物流科技有限公司 | Financial management method and system based on logistics industry chain |
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